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Title:
kd-tree based parallel adaptive rendering
Author: Liu Xiao-Dan ; Wu Jia-Ze ; Zheng Chang-Wen
Source: Visual Computer
Issued Date: 2012
Volume: 28, Issue:41068, Pages:613-623
Indexed Type: ei
Department: (1) National Key Laboratory of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Graduate University of Chinese Academy of Sciences, Beijing, China
English Abstract: Multidimensional adaptive sampling technique is crucial for generating high quality images with effects such as motion blur, depth-of-field and soft shadows, but it costs a lot of memory and computation time. We propose a novel kd-tree based parallel adaptive rendering approach. First, a two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and control the memory cost: in the prepare stage, we coarsely sample the entire multidimensional space and use kd-tree structure to separate it into several multidimensional subspaces; in the main stage, each subspace is refined by a sub kd-tree and rendered in parallel. Second, novel kd-tree based strategies are introduced to measure space's error value and generate anisotropic Poisson disk samples. The experimental results show that our algorithm produces better quality images than previous ones. © 2012 Springer-Verlag.
Language: 英语
WOS ID: WOS:000304411500010
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14708
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Liu Xiao-Dan,Wu Jia-Ze,Zheng Chang-Wen. kd-tree based parallel adaptive rendering[J]. Visual Computer,2012-01-01,28(41068):613-623.
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